Japanese OCR Error Correction using Character Shape Similarity and Statistical Language Model

We present a novel OCR error correction method for languages without word delimiters that have a large character set, such as Japanese and Chinese. It consists of a statistical OCR model, an approximate word matching method using character shape similarity, and a word segmentation algorithm using a statistical language model. By using a statistical OCR model and character shape similarity, the proposed error corrector outperforms the previously published method. When the baseline character recognition accuracy is 90%, it achieves 97.4% character recognition accuracy.

• Type: Conference
• Year: 1998
• Where: ACL
• Authors: Masaaki Nagata
• Keywords: ACL 1998, ACL 2007, Character Recognition Accuracy, Character Shape Similarity, Statistical Ocr Model